The document discusses the analysis of historical black box sensor records and maintenance events for aircraft, utilizing machine learning models to predict maintenance needs. It highlights the rapid growth of data science from 2011 to 2016, emphasizing a significant increase in data generation and the importance of bridging the talent gap in the industry. Additionally, it references various tools and platforms like R, Python, and Azure for data analysis and modeling.

5. Historical black box sensor
records and maintenance events
for many aircraft
Train and compare various models to predict maintenance
events
Scoring rules to predict likely
maintenance events from sensor
data

10. xgboost Vowpal WabbitRattle
CNTK

15. cloud
computing
2011  2016 5x increase
data
science
Universities filling
300,000 US talent gap
90% of the data in the world
today has been created in
the last two years alone
big
data
open
source
including R, Python, Linux, Hadoop,
Spark, …

16. David Smith
R Community Lead
Microsoft
@revodavid
davidsmi@microsoft.com
Revolutions blog
blog.revolutionanalytics.com

17. Thank you!
David Smith
R Community Lead
Microsoft
@revodavid
davidsmi@microsoft.com
Revolutions blog
blog.revolutionanalytics.com
blog.revolutionanalytics.com/2016/02/xbox_usage_trends_r.html
www.microsoft.com/en-us/stories/88acres
powerbi.microsoft.com/en-us/industries/airline
blog.revolutionanalytics.com/2016/03/sql-server-2016-launch.html
studio.azureml.net
github.com/RevolutionAnalytics/AzureML
blog.revolutionanalytics.com/2016/03/scoring-r-models-with-excel.html
www.visualstudio.com/en-us/features/rtvs-vs.aspx
mran.microsoft.com/download